Taming nucleon density distributions with deep neural network
Taming nucleon density distributions with deep neural network
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DOI：
10.1016/j.physletb.2021.136650
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发表时间：
2020-12
影响因子：
4.4
通讯作者：
Zu-Xing Yang;Xiao-Hua Fan;P. Yin;W. Zuo
中科院分区：
文献类型：
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作者：
Zu-Xing Yang;Xiao-Hua Fan;P. Yin;W. Zuo
利用Skyrme密度泛函理论计算的密度分布数据集，我们详细阐述了深度神经网络来生成密度分布，并为其他结构模型的类似应用提供了相关超参数集的表格。在以均方误差和Kullback-Leibler散度（交叉熵）为目标函数的机器学习过程中，存在一个从类费米分布向真实Skyrme分布过渡的转折点，而当采用Pearson χ 2散度时，这一特性被超越。一个大约35分钟的训练程序，只有大约5%-10%的核（200 - 300）就足以描述所有核图表的核子密度分布，相对误差在2%以内。我们得到类似的结果，采用不同的数据集计算不同的Skyrme密度泛函理论。我们进一步研究了外推性质，结果表明增加15个核子是可以接受的。基于这些结果，我们提出了一种混合数据集方法和再训练方法，以超越单一的物理结构模型。
With the datasets of the density distributions calculated by Skyrme density functional theories, we elaborated deep neural networks to generate the density profile and provide a table of related hyperparameters set for similar applications of other structural models. In the process of machine learning with the objective/target functions that normalized mean square error and Kullback–Leibler divergence (cross entropy), there is a turning point showing the transition from the Fermi-like distribution to the realistic Skyrme distribution, while this property is transcended when Pearson χ 2 divergence is employed. A training program of about 35 minutes with only about 5%− 10% nuclei (200− 300) is sufficient to describe the nucleon density distributions of all the nuclear chart within 2% relative error. We obtain similar results employing different datasets calculated by different Skyrme density functional theories. We further investigate the extrapolation properties, which show that an addition of 15 nucleons is acceptable. Based on the results, we propose a mixed dataset approach and a retraining approach in order to go beyond a single physical structure model.